langsmith-deployment
LangSmith Deployment
Use this skill to deploy, revise, monitor, and troubleshoot LangGraph-based agents in LangSmith Deployment.
Use This Skill When
- You need to deploy a new agent to LangSmith Cloud.
- You need to create a new deployment revision from Git changes or env changes.
- You need rollback guidance for a failing revision.
- You need to choose deployment model: Cloud, Hybrid/Self-hosted with control plane, or Standalone server.
- You need CI/CD automation using LangSmith Deployment control-plane APIs.
- You need monitoring and alert setup aligned with current LangSmith alert model.
- You need
langgraph.jsonvalidation and deployment compatibility checks.
Deployment Model Selection
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45langgraph-state-management
Design state schemas, implement reducers, configure persistence, and debug state issues for LangGraph applications. Use when users want to (1) design or define state schemas for LangGraph graphs, (2) implement reducer functions for state accumulation, (3) configure persistence with checkpointers (InMemorySaver/MemorySaver, SqliteSaver, PostgresSaver), (4) debug state update issues or unexpected state behavior, (5) migrate state schemas between versions, (6) validate state schema structure, (7) choose between TypedDict and MessagesState patterns, (8) implement custom reducers for lists, dicts, or sets, (9) use the Overwrite type to bypass reducers, (10) set up thread-based persistence for multi-turn conversations, or (11) inspect checkpoints for debugging.
26langgraph-error-handling
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25langgraph-testing-evaluation
Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.
24langgraph-project-setup
Initialize and configure LangGraph projects with proper structure, langgraph.json configuration, environment variables, and dependency management. Use when users want to (1) create a new LangGraph project, (2) set up langgraph.json for deployment, (3) configure environment variables for LLM providers, (4) initialize project structure for agents, (5) set up local development with LangGraph Studio, (6) configure dependencies (pyproject.toml, requirements.txt, package.json), or (7) troubleshoot project configuration issues.
21deepagents-setup-configuration
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.
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